## data extracted from New York Times state-level data from NYT Github repository
# https://github.com/nytimes/covid-19-data
## state-level population information from us_census_data available on GitHub repository:
# https://github.com/COVID19Tracking/associated-data/tree/master/us_census_data
### FINISH THE CODE HERE ###
# load COVID state-level data from NYT
cv_states <- read.csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv")
### FINISH THE CODE HERE ###
# load state population data
state_pops <- read.csv("https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv")
# adjust column names
state_pops$abb <- state_pops$state
state_pops$state <- state_pops$state_name
state_pops$state_name <- NULL
### FINISH THE CODE HERE
cv_states <- merge(cv_states,state_pops, by="state")lab10
Lab 10 - Interactive Visualization
We recommend downloading this QMD file to use as a template for your answers. You can download the file from: https://github.com/USCbiostats/PM566/blob/main/labs/lab9.qmd
We have set eval=FALSE as a global option.
To run a specific chunk, you can set eval=TRUE in that chunk.
To run all chunks, you can set eval=TRUE inside of opts_chunk$set() in the setup chunk.
Learning Goals
Read in and process the COVID dataset from the New York Times GitHub repository
Create interactive graphs of different types using
plot_ly()andggplotly()functionsCustomize the hoverinfo and other plot features
Create a Choropleth map using
plot_geo()
Lab Description
We will work with COVID data downloaded from the New York Times. The dataset consists of COVID-19 cases and deaths in each US state during the course of the COVID epidemic.
The objective of this lab is to explore relationships between cases, deaths, and population sizes of US states, and plot data to demonstrate this
Steps
I. Reading and processing the New York Times (NYT) state-level COVID-19 data
1. Read in the data
Read in the COVID data with
read.csv()from the NYT GitHub repository: https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csvRead in the state population data with
read.csv()from the repository: https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csvMerge datasets
2. Look at the data
Inspect the dimensions,
head, andtailof the dataInspect the structure of each variables
dim(cv_states)[1] 58094 9head(cv_states)state date fips cases deaths geo_id population pop_density abb 1 Alabama 2023-01-04 1 1587224 21263 1 4887871 96.50939 AL 2 Alabama 2020-04-25 1 6213 213 1 4887871 96.50939 AL 3 Alabama 2023-02-26 1 1638348 21400 1 4887871 96.50939 AL 4 Alabama 2022-12-03 1 1549285 21129 1 4887871 96.50939 AL 5 Alabama 2020-05-06 1 8691 343 1 4887871 96.50939 AL 6 Alabama 2021-04-21 1 524367 10807 1 4887871 96.50939 ALtail(cv_states)state date fips cases deaths geo_id population pop_density abb 58089 Wyoming 2022-09-11 56 175290 1884 56 577737 5.950611 WY 58090 Wyoming 2022-08-21 56 173487 1871 56 577737 5.950611 WY 58091 Wyoming 2021-01-26 56 51152 596 56 577737 5.950611 WY 58092 Wyoming 2021-02-21 56 53795 662 56 577737 5.950611 WY 58093 Wyoming 2021-08-22 56 70671 809 56 577737 5.950611 WY 58094 Wyoming 2021-03-20 56 55581 693 56 577737 5.950611 WYstr(cv_states)'data.frame': 58094 obs. of 9 variables: $ state : chr "Alabama" "Alabama" "Alabama" "Alabama" ... $ date : chr "2023-01-04" "2020-04-25" "2023-02-26" "2022-12-03" ... $ fips : int 1 1 1 1 1 1 1 1 1 1 ... $ cases : int 1587224 6213 1638348 1549285 8691 524367 1321892 1088370 1153149 814025 ... $ deaths : int 21263 213 21400 21129 343 10807 19676 16756 16826 15179 ... $ geo_id : int 1 1 1 1 1 1 1 1 1 1 ... $ population : int 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ... $ pop_density: num 96.5 96.5 96.5 96.5 96.5 ... $ abb : chr "AL" "AL" "AL" "AL" ...
3. Format the data
Make date into a date variable
Make state into a factor variable
Order the data first by state, second by date
Confirm the variables are now correctly formatted
Inspect the range values for each variable
# format the date cv_states$date <- as.Date(cv_states$date, format="%Y-%m-%d") # format the state and state abbreviation (abb) variables state_list <- unique(cv_states$state) cv_states$state <- factor(cv_states$state, levels = state_list) abb_list <- unique(cv_states$abb) cv_states$abb <- factor(cv_states$abb, levels = abb_list) ### FINISH THE CODE HERE # order the data first by state, second by date cv_states <- cv_states[order(cv_states$state,cv_states$date),] # Confirm the variables are now correctly formatted str(cv_states)'data.frame': 58094 obs. of 9 variables: $ state : Factor w/ 52 levels "Alabama","Alaska",..: 1 1 1 1 1 1 1 1 1 1 ... $ date : Date, format: "2020-03-13" "2020-03-14" ... $ fips : int 1 1 1 1 1 1 1 1 1 1 ... $ cases : int 6 12 23 29 39 51 78 106 131 157 ... $ deaths : int 0 0 0 0 0 0 0 0 0 0 ... $ geo_id : int 1 1 1 1 1 1 1 1 1 1 ... $ population : int 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ... $ pop_density: num 96.5 96.5 96.5 96.5 96.5 ... $ abb : Factor w/ 52 levels "AL","AK","AZ",..: 1 1 1 1 1 1 1 1 1 1 ...head(cv_states)state date fips cases deaths geo_id population pop_density abb 1029 Alabama 2020-03-13 1 6 0 1 4887871 96.50939 AL 597 Alabama 2020-03-14 1 12 0 1 4887871 96.50939 AL 282 Alabama 2020-03-15 1 23 0 1 4887871 96.50939 AL 12 Alabama 2020-03-16 1 29 0 1 4887871 96.50939 AL 266 Alabama 2020-03-17 1 39 0 1 4887871 96.50939 AL 78 Alabama 2020-03-18 1 51 0 1 4887871 96.50939 ALtail(cv_states)state date fips cases deaths geo_id population pop_density abb 57902 Wyoming 2023-03-18 56 185640 2009 56 577737 5.950611 WY 57916 Wyoming 2023-03-19 56 185640 2009 56 577737 5.950611 WY 57647 Wyoming 2023-03-20 56 185640 2009 56 577737 5.950611 WY 57867 Wyoming 2023-03-21 56 185800 2014 56 577737 5.950611 WY 58057 Wyoming 2023-03-22 56 185800 2014 56 577737 5.950611 WY 57812 Wyoming 2023-03-23 56 185800 2014 56 577737 5.950611 WY# Inspect the range values for each variable. head(cv_states)state date fips cases deaths geo_id population pop_density abb 1029 Alabama 2020-03-13 1 6 0 1 4887871 96.50939 AL 597 Alabama 2020-03-14 1 12 0 1 4887871 96.50939 AL 282 Alabama 2020-03-15 1 23 0 1 4887871 96.50939 AL 12 Alabama 2020-03-16 1 29 0 1 4887871 96.50939 AL 266 Alabama 2020-03-17 1 39 0 1 4887871 96.50939 AL 78 Alabama 2020-03-18 1 51 0 1 4887871 96.50939 ALsummary(cv_states)state date fips cases Washington : 1158 Min. :2020-01-21 Min. : 1.00 Min. : 1 Illinois : 1155 1st Qu.:2020-12-06 1st Qu.:16.00 1st Qu.: 112125 California : 1154 Median :2021-09-11 Median :29.00 Median : 418120 Arizona : 1153 Mean :2021-09-10 Mean :29.78 Mean : 947941 Massachusetts: 1147 3rd Qu.:2022-06-17 3rd Qu.:44.00 3rd Qu.: 1134318 Wisconsin : 1143 Max. :2023-03-23 Max. :72.00 Max. :12169158 (Other) :51184 deaths geo_id population pop_density Min. : 0 Min. : 1.00 Min. : 577737 Min. : 1.292 1st Qu.: 1598 1st Qu.:16.00 1st Qu.: 1805832 1st Qu.: 43.659 Median : 5901 Median :29.00 Median : 4468402 Median : 107.860 Mean : 12553 Mean :29.78 Mean : 6397965 Mean : 423.031 3rd Qu.: 15952 3rd Qu.:44.00 3rd Qu.: 7535591 3rd Qu.: 229.511 Max. :104277 Max. :72.00 Max. :39557045 Max. :11490.120 NA's :1106 abb WA : 1158 IL : 1155 CA : 1154 AZ : 1153 MA : 1147 WI : 1143 (Other):51184min(cv_states$date)[1] "2020-01-21"max(cv_states$date)[1] "2023-03-23"
4. Add new_cases and new_deaths and correct outliers
Add variables for new cases,
new_cases, and new deaths,new_deaths:- Hint: You can set
new_casesequal to the difference between cases on date i and date i-1, starting on date i=2
- Hint: You can set
Filter to dates after June 1, 2021
Use
plotlyfor EDA: See if there are outliers or values that don’t make sense fornew_casesandnew_deaths. Which states and which dates have strange values?Correct outliers: Set negative values for
new_casesornew_deathsto 0Recalculate
casesanddeathsas cumulative sum of updatednew_casesandnew_deathsGet the rolling average of new cases and new deaths to smooth over time
Inspect data again interactively
# Add variables for new_cases and new_deaths: for (i in 1:length(state_list)) { cv_subset <- subset(cv_states, state == state_list[i]) cv_subset <- cv_subset[order(cv_subset$date),] # add starting level for new cases and deaths cv_subset$new_cases <- cv_subset$cases[1] cv_subset$new_deaths <- cv_subset$deaths[1] ### FINISH THE CODE HERE for (j in 2:nrow(cv_subset)) { cv_subset$new_cases[j] <- cv_subset$cases[j] - cv_subset$cases[j-1] cv_subset$new_deaths[j] <- cv_subset$deaths[j] - cv_subset$deaths[j-1] } # include in main dataset cv_states$new_cases[cv_states$state==state_list[i]] <- cv_subset$new_cases cv_states$new_deaths[cv_states$state==state_list[i]] <- cv_subset$new_deaths } # Focus on recent dates cv_states <- cv_states |> dplyr::filter(date >= "2021-06-01") #install.packages("plotly") install.packages("zoo")The following package(s) will be installed: - zoo [1.8-14] These packages will be installed into "~/Desktop/PM566labs/renv/library/macos/R-4.4/aarch64-apple-darwin20". # Installing packages -------------------------------------------------------- - Installing zoo ... OK [linked from cache] Successfully installed 1 package in 7.9 milliseconds.library(plotly) library(ggplot2) library(zoo) # Inspect outliers in new_cases using plotly p1 <- ggplot(cv_states, aes(x = date, y = new_cases, color = state)) + geom_point(size = .5, alpha = 0.5) ggplotly(p1)p2 <- ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + geom_point(size = .5, alpha = 0.5) ggplotly(p2)# set negative new case or death counts to 0 cv_states$new_cases[cv_states$new_cases<0] <- 0 cv_states$new_deaths[cv_states$new_deaths<0] <- 0 # Re-calculate `cases` and `deaths` as cumulative sum of updated `new_cases` and `new_deaths` for (i in 1:length(state_list)) { cv_subset = subset(cv_states, state == state_list[i]) # add starting level for new cases and deaths cv_subset$cases <- cv_subset$cases[1] cv_subset$deaths <- cv_subset$deaths[1] ### FINISH CODE HERE for (j in 2:nrow(cv_subset)) { cv_subset$cases[j] <- cv_subset$new_cases[j] + cv_subset$cases[j-1] cv_subset$deaths[j] <- cv_subset$new_deaths[j] + cv_subset$deaths[j-1] } # include in main dataset cv_states$cases[cv_states$state==state_list[i]] <- cv_subset$cases cv_states$deaths[cv_states$state==state_list[i]] <- cv_subset$deaths } # Smooth new counts cv_states$new_cases <- zoo::rollmean(cv_states$new_cases, k=7, fill=NA, align='right') |> round(digits = 0) cv_states$new_deaths <- zoo::rollmean(cv_states$new_deaths, k=7, fill=NA, align='right') |> round(digits = 0) # Inspect data again interactively p2 <- ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5) ggplotly(p2)
5. Add additional variables
Add population-normalized (by 100,000) variables for each variable type (rounded to 1 decimal place). Make sure the variables you calculate are in the correct format (
numeric). You can use the following variable names:per100k= cases per 100,000 populationnewper100k= new cases per 100,000deathsper100k= deaths per 100,000newdeathsper100k= new deaths per 100,000
Add a
naive CFRvariable representingdeaths / caseson each date for each stateCreate a data frame representing values on the most recent date,
cv_states_today, as done in lecture### FINISH CODE HERE # add population normalized (by 100,000) counts for each variable cv_states$per100k <- as.numeric(format(round(cv_states$cases/(cv_states$population/100000),1),nsmall=1)) cv_states$newper100k <- as.numeric(format(round(cv_states$new_cases/(cv_states$population/100000),1),nsmall=1)) cv_states$deathsper100k <- as.numeric(format(round(cv_states$deaths/(cv_states$population/100000),1),nsmall=1)) cv_states$newdeathsper100k <- as.numeric(format(round(cv_states$new_deaths/(cv_states$population/100000),1),nsmall=1)) # add a naive_CFR variable = deaths / cases cv_states <- cv_states |> mutate(naive_CFR = round((deaths*100/cases),2)) # create a `cv_states_today` variable cv_states_today <- subset(cv_states, date==max(cv_states$date))
II. Scatterplots
6. Explore scatterplots using plot_ly()
Create a scatterplot using
plot_ly()representingpop_densityvs. various variables (e.g.cases,per100k,deaths,deathsper100k) for each state on most recent date (cv_states_today)Color points by state and size points by state population
Use hover to identify any outliers.
Remove those outliers and replot.
Choose one plot. For this plot:
Add hoverinfo specifying the state name, cases per 100k, and deaths per 100k, similarly to how we did this in the lecture notes
Add layout information to title the chart and the axes
Enable
hovermode = "compare"cv_states_today |> plot_ly(x = ~pop_density, y = ~cases, type = "scatter", mode = 'markers', color = ~state, size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))# filter out "District of Columbia" cv_states_today_filter <- cv_states_today |> filter(state!="District of Columbia") # pop_density vs. cases after filtering cv_states_today_filter |> plot_ly(x = ~pop_density, y = ~cases, type = "scatter", mode = 'markers', color = ~state, size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))# pop_density vs. deathsper100k cv_states_today_filter |> plot_ly(x = ~pop_density, y = ~deathsper100k, type = "scatter", mode = 'markers', color = ~state, size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))# Adding hoverinfo cv_states_today_filter |> plot_ly(x = ~pop_density, y = ~deathsper100k, type ="scatter", mode = 'markers', color = ~state, size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5), hoverinfo = 'text', text = ~paste( paste(state, ":", sep=""), paste(" Cases per 100k: ", per100k, sep="") , paste(" Deaths per 100k: ", deathsper100k, sep=""), sep = "<br>")) |> layout(title = "Population-normalized COVID-19 deaths (per 100k) vs. population density for US states", yaxis = list(title = "Deaths per 100k"), xaxis = list(title = "Population Density"), hovermode = "compare")
7. Explore scatterplot trend interactively using ggplotly() and geom_smooth()
For
pop_densityvs.newdeathsper100kcreate a chart with the same variables usinggglot_ly()Explore the pattern between and using
geom_smooth()### FINISH CODE HERE p <- ggplot(cv_states_today_filter, aes(x=pop_density, y=deathsper100k, size=population)) + geom_point() + geom_smooth() ggplotly(p)
8. Multiple line chart
Create a line chart of the
naive_CFRfor all states over time usingplot_ly()- Use the zoom and pan tools to inspect the
naive_CFRfor the states that had an increase in September.
- Use the zoom and pan tools to inspect the
Create one more line chart, for Florida only, which shows
new_casesandnew_deathstogether in one plot. Hint: look for anadd_*()Use hoverinfo to “eyeball” the approximate peak of deaths and peak of cases. What is the time delay between the peak of cases and the peak of deaths?
### FINISH CODE HERE # Line chart for naive_CFR for all states over time using `plot_ly()` plot_ly(cv_states, x = ~date, y = ~naive_CFR, color = ~state, type = "scatter", mode = "lines")### FINISH CODE HERE # Line chart for Florida showing new_cases and new_deaths together (two lines) cv_states |> filter(state=="Florida") |> plot_ly(x = ~date, y = ~new_cases, type = "scatter", mode = "lines") |> add_lines(x = ~date, y = ~new_deaths, type = "scatter", mode = "lines")
9. Heatmaps
Create a heatmap to visualize new_cases for each state on each date greater than June 1st, 2021 - Start by mapping selected features in the dataframe into a matrix using the tidyr package function pivot_wider(), naming the rows and columns, as done in the lecture notes - Use plot_ly() to create a heatmap out of this matrix. Which states stand out? - Repeat with newper100k variable. Now which states stand out? - Create a second heatmap in which the pattern of new_cases for each state over time becomes more clear by filtering to only look at dates every two weeks
### FINISH CODE HERE
# Map state, date, and new_cases to a matrix
library(tidyr)
cv_states_mat <- cv_states |> select(state, date, new_cases) |> dplyr::filter(date>as.Date("2021-06-15"))
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = new_cases))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)
# Create a heatmap using plot_ly()
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
z=~cv_states_mat2,
type="heatmap",
showscale=T)# Repeat with newper100k
cv_states_mat <- cv_states |> select(state, date, newper100k) |> dplyr::filter(date>as.Date("2021-06-15"))
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = newper100k))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
z=~cv_states_mat2,
type="heatmap",
showscale=T)# Create a second heatmap after filtering to only include dates every other week
filter_dates <- seq(as.Date("2021-06-15"), as.Date("2021-11-01"), by="2 weeks")
cv_states_mat <- cv_states |> select(state, date, newper100k) |> filter(date %in% filter_dates)
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = newper100k))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)
# Create a heatmap using plot_ly()
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
z=~cv_states_mat2,
type="heatmap",
showscale=TRUE)10. Map
Create a map to visualize the
naive_CFRby state on October 15, 2021Compare with a map visualizing the
naive_CFRby state on most recent datePlot the two maps together using
subplot(). Make sure the shading is for the same range of values (google is your friend for this)Describe the difference in the pattern of the CFR.
### For specified date pick.date <- "2021-10-15" # Extract the data for each state by its abbreviation cv_per100 <- cv_states |> filter(date==pick.date) |> select(state, abb, newper100k, cases, deaths) # select data cv_per100$state_name <- cv_per100$state cv_per100$state <- cv_per100$abb cv_per100$abb <- NULL # Create hover text cv_per100$hover <- with(cv_per100, paste(state_name, '<br>', "Cases per 100k: ", newper100k, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths)) # Set up mapping details set_map_details <- list( scope = 'usa', projection = list(type = 'albers usa'), showlakes = TRUE, lakecolor = toRGB('white') ) # Make sure both maps are on the same color scale shadeLimit <- 125 # Create the map fig <- plot_geo(cv_per100, locationmode = 'USA-states') |> add_trace( z = ~newper100k, text = ~hover, locations = ~state, color = ~newper100k, colors = 'Purples' ) fig <- fig |> colorbar(title = paste0("Cases per 100k: ", pick.date), limits = c(0,shadeLimit)) fig <- fig |> layout( title = paste('Cases per 100k by State as of ', pick.date, '<br>(Hover for value)'), geo = set_map_details ) fig_pick.date <- fig ############# ### Map for today's date # Extract the data for each state by its abbreviation cv_per100 <- cv_states_today |> select(state, abb, newper100k, cases, deaths) # select data cv_per100$state_name <- cv_per100$state cv_per100$state <- cv_per100$abb cv_per100$abb <- NULL # Create hover text cv_per100$hover <- with(cv_per100, paste(state_name, '<br>', "Cases per 100k: ", newper100k, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths)) # Set up mapping details set_map_details <- list( scope = 'usa', projection = list(type = 'albers usa'), showlakes = TRUE, lakecolor = toRGB('white') ) # Create the map fig <- plot_geo(cv_per100, locationmode = 'USA-states') |> add_trace( z = ~newper100k, text = ~hover, locations = ~state, color = ~newper100k, colors = 'Purples' ) fig <- fig |> colorbar(title = paste0("Cases per 100k: ", Sys.Date()), limits = c(0,shadeLimit)) fig <- fig |> layout( title = paste('Cases per 100k by State as of', Sys.Date(), '<br>(Hover for value)'), geo = set_map_details ) fig_Today <- fig ### Plot together subplot(fig_pick.date, fig_Today, nrows = 2, margin = .05)
The early-pandemic CFR map shows strong regional differences, with several northern and western states experiencing disproportionately high fatality ratios. By the most recent date, CFR becomes much lower and far more uniform across states, reflecting improvements in treatment, testing, and population immunity.